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IoT Workflow Scheduling Using Intelligent Arithmetic Optimization Algorithm in Fog Computing.

Mohamed Abd Elaziz1,2,3,4,5, Laith Abualigah6,7, Rehab Ali Ibrahim1

  • 1Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt.

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Summary
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This study introduces AOAM, an enhanced energy-aware model for Internet of Things (IoT) task scheduling in fog computing. It optimizes Quality of Service (QoS) by improving makespan and reducing energy consumption, outperforming existing methods.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Computing

Background:

  • Internet of Things (IoT) applications require high Quality of Service (QoS).
  • Fog computing offers a decentralized alternative to cloud computing for IoT, but faces challenges with resource availability and computational overhead.
  • Efficient task scheduling is crucial for optimizing fog computing performance.

Purpose of the Study:

  • To propose an energy-aware model for job scheduling in fog computing environments.
  • To enhance the Arithmetic Optimization Algorithm (AOA) with Marine Predators Algorithm (MPA) operators, creating AOAM.
  • To maximize user QoS by optimizing the makespan and minimizing energy consumption.

Main Methods:

  • Developed an enhanced Arithmetic Optimization Algorithm (AOAM) by integrating Marine Predators Algorithm (MPA) search operators into the AOA.
  • Implemented an energy-aware model for task scheduling in fog computing.
  • Validated the AOAM model using diverse parameters including clients, data centers, hosts, virtual machines, and tasks.
  • Evaluated performance based on energy consumption and makespan metrics.

Main Results:

  • The proposed AOAM model effectively addresses task scheduling challenges in fog computing.
  • AOAM demonstrated improved performance in maximizing makespan and managing energy consumption.
  • Comparative analysis showed AOAM significantly outperforms other state-of-the-art methods.

Conclusions:

  • The AOAM model offers a promising solution for efficient task scheduling in fog computing.
  • The integration of MPA operators enhances AOA's ability to overcome solution diversity and local optimum issues.
  • The study highlights the potential of AOAM for improving QoS in IoT applications within fog environments.